Difference Between Classification and Clustering
| S.No | Classification | Clustering |
|---|---|---|
| 1. | Supervised learning | Unsupervised learning |
| 2. | Works with labelled data | Works with unlabelled data |
| 3. | Requires prior knowledge or domain expertise | No prior knowledge is needed |
| 4. | Labels once assigned do not change | Cluster results can be dynamic and change with data updates |
| 5. | Trial-and-error method is not common | Involves trial-and-error to form meaningful clusters |
Applications of Clustering
- Customer Segmentation based on buying patterns or lifestyle.
- Document Retrieval in search engines or archives.
- Gene Grouping in biomedical research for disease influence analysis.
- Organ Similarity based on physiological functions.
- Biological Taxonomy – classification of animals and plants.
- Demographic Segmentation in marketing.
- Document Indexing for quick searching.
- Data Compression by grouping similar or duplicate items.
Challenges in Clustering Algorithms
- High-dimensional data can make clustering ineffective (scaling issue).
- Huge data volumes (especially due to the internet) make computation difficult.
- Inconsistent data units (e.g., kg vs. pounds) can distort results.
- Designing a good proximity measure (similarity metric) is often complex.
- Cluster interpretability and validation can be challenging.
Advantages and Disadvantages of Clustering Algorithms
| S.No | Advantages | Disadvantages |
|---|---|---|
| 1. | Can handle missing data and outliers | Sensitive to initial values and data order |
| 2. | Helps in semi-supervised learning to label unlabelled data | Requires user to pre-specify number of clusters |
| 3. | Easy to explain and implement | Scaling issues in high-dimensional data |
| 4. | Clustering is a well-known statistical technique | Designing similarity/proximity measures can be challenging |